This invention relates in general to a word recognition apparatus and method, and in particular, to a word recognition method an apparatus for recognizing one word in the context of adjacent words.
The recognition of images of words of text is a difficult problem, especially when the image texts are degraded by noise such as that introduced by photocopying or facsimile transmission. Recently, methods for improving the quality of image text recognition results have focused on the use of knowledge about the language in which the document is written. J. J. Hull, S. Khoubyari and T. K. Ho, "Word Image Matching as a Technique for Degraded Text Recognition," in Proceedings of 11th IAPR International Conference on Pattern Recognition, The Hague, The Netherlands, pp. 665-668, 1992; J. J. Hull, "A Hidden Markov Model for Language Syntax in Text Recognition," in Proceedings of 11th IAPR International Conference on Pattern Recognition, The Hague, The Netherlands, pp. 124-127, 1992. These techniques often post-process the results of a word recognition process that provides various alternatives for the identity of each word that are called its neighborhood. The objective of the language model is to choose the alternatives for words that make sense in the context of the rest of the text.
Word collocation data is one source of information that has been investigated in computational linguistics and that has been proposed as a useful tool to post-process word recognition results. H. S. Baird, Private communication about the use of word collocation to improve OCR results, February, 1989; K. W. Church and P. Hanks, "Word Association Norms, Mutual Information, and Lexicography," Computational Linguistics, Vol. 16, No. 1, pp. 22-29, 1990; T. G. Rose, R. J. Whitrow and L. J. Evett, "The Use of Semitic Information as an Aid to Handwriting Recognition," in Proceedings of 1st International Conference of Document Analysis and Recognition, pp. 629-637, Saint-Malo, France, 1991. Word collocation refers to the likelihood that two words co-occur within a fixed distance of one another. For example, it is highly likely that if the word "boat" occurs, the word "river" will also occur somewhere in ten words on either side of "boat." Thus, if "river" had been misrecognized with the neighborhood "rover, river, ripper" (i.e., rover is the top choice, river the second choice, etc.), the presence of "boat" nearby would allow for the recognition error to be corrected.
Previous work using word collocation data to post-process word recognition results has shown the usefulness of this data. T. G. Rose and L. J. Evett, "Text Recognition Using Collocations and Domain Codes," in Proceedings of the Workshop on Very Large Corpora: Academic and Industrial Perspectives, pp. 65-73, Columbus, Ohio, 1993. This technique used local collocation data about words that co-occur next to each other to improve recognition performance. A disadvantage of this approach was that it did not allow for successful results off:one word to influence the results on another word. In other fields such as in edge detection of an image, it has been proposed to use relaxation techniques. See, e.g., A. Rosenfeld, R. A. Hummel and S. W. Zucker, "Scene Labeling by Relaxation Operations," in IEEE Trans. on Sys. Man and Cyb, SMC-6(6):420-433, 1976.